{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第七次练习"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "+ 请务必交到exer7文件夹下，**谢绝交到master下**\n",
    "+ 请不要改动任何文件，拜托\n",
    "+ 请在12月20日前提交。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "请写一下姓名和学号：\n",
    "+ 姓名 黄鹏辉\n",
    "+ 学号 0161918"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "plt.rcParams['font.sans-serif']=['SimHei'] \n",
    "plt.rcParams['axes.unicode_minus']=False \n",
    "from matplotlib.ticker import MultipleLocator, FormatStrFormatter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\97657\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (20,22,23,25,28,29,39,49,74,124,125,126,127,128,129,130,131,138,140,141,147,160,161,162,165,170,174,175,176,177,179,180,181,182,183,184,188,191,194,195,196,199,221,222,223,224,251,252,254,289,290,294,295,296) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ids</th>\n",
       "      <th>clsids</th>\n",
       "      <th>schids</th>\n",
       "      <th>ctyids</th>\n",
       "      <th>frame</th>\n",
       "      <th>subsample</th>\n",
       "      <th>sweight</th>\n",
       "      <th>fall</th>\n",
       "      <th>grade9</th>\n",
       "      <th>stcog</th>\n",
       "      <th>...</th>\n",
       "      <th>steco_3c</th>\n",
       "      <th>stonly</th>\n",
       "      <th>stsib</th>\n",
       "      <th>stsibrank</th>\n",
       "      <th>stmedu</th>\n",
       "      <th>stfedu</th>\n",
       "      <th>stprhedu</th>\n",
       "      <th>stfdrunk</th>\n",
       "      <th>stprfight</th>\n",
       "      <th>stprrel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>218.738892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>216.518234</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>216.518234</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>218.738892</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>217.553040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ids  clsids  schids  ctyids  frame  subsample     sweight  fall  grade9  \\\n",
       "0    1       1       1       1      3          3  218.738892     0       0   \n",
       "1    2       1       1       1      3          3  216.518234     0       0   \n",
       "2    3       1       1       1      3          3  216.518234     0       0   \n",
       "3    4       1       1       1      3          3  218.738892     0       0   \n",
       "4    5       1       1       1      3          3  217.553040     0       0   \n",
       "\n",
       "   stcog   ...    steco_3c stonly stsib stsibrank stmedu stfedu stprhedu  \\\n",
       "0     11   ...           3      1                      3      3        3   \n",
       "1     17   ...           2      1                      8      5        8   \n",
       "2     12   ...           2      2     1         3      3      3        3   \n",
       "3     10   ...           2      1                      6      7        7   \n",
       "4     10   ...           3      1                      7      8        8   \n",
       "\n",
       "  stfdrunk stprfight stprrel  \n",
       "0        1         1       2  \n",
       "1        1         1       2  \n",
       "2        1         1       1  \n",
       "3        1         1       2  \n",
       "4        1         1       2  \n",
       "\n",
       "[5 rows x 300 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('CEPS.csv',encoding='gb2312')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "下面的图都至少需要在图上标注：\n",
    "+ 图标题\n",
    "+ x轴标题\n",
    "+ y轴标题\n",
    "+ 适当修改x轴或者y轴的刻度及标签，使之清晰美观\n",
    "+ 根据需要添加图例"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 散点图\n",
    "反映期中考试标准化成绩语文（stdchn）和期中考试标准化成绩数学（stdmat）的相关关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xminorLocator   = MultipleLocator(5) #将x主刻度标签设置为0.5的倍数\n",
    "xminorFormatter = FormatStrFormatter('%.1f') #设置x轴标签文本的格式\n",
    "\n",
    "ymajorLocator   = MultipleLocator(5) #将y轴主刻度标签设置为0.5的倍数\n",
    "ymajorFormatter = FormatStrFormatter('%.1f') #设置y轴标签文本的格式\n",
    "\n",
    "fig=plt.figure()\n",
    "ax1=fig.add_subplot(111)\n",
    "ax1.set_title(\"语文与数学散点图\", fontdict=None, loc='center')\n",
    "x=df.stdchn[:150]\n",
    "y=df.stdmat[:150]\n",
    "ax1.set_xlabel('语文成绩')\n",
    "ax1.set_ylabel('数学成绩')\n",
    "ax1.xaxis.set_major_locator(xminorLocator )\n",
    "ax1.xaxis.set_major_formatter(xminorFormatter)\n",
    "\n",
    "ax1.yaxis.set_major_locator(ymajorLocator)\n",
    "ax1.yaxis.set_major_formatter(ymajorFormatter)\n",
    "ax1.set_xlim(0,100)\n",
    "ax1.set_ylim(0,100)\n",
    "\n",
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 饼图\n",
    "对问题“你是独生子女吗”(b01)的回答有“是”和“否”两种回答，相应的数字分别是1和2。请画一个饼图反映二者的比例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11295"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(df.b01==1)+sum(df.b01==2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = '是', '否'\n",
    "x=sum(df.b01==1)/(sum(df.b01==1)+sum(df.b01==2))\n",
    "y=sum(df.b01==2)/(sum(df.b01==1)+sum(df.b01==2))\n",
    "sizes = [x,y]\n",
    "\n",
    "fig2, ax2 = plt.subplots()\n",
    "ax2.set_title(\"是否是独生子女饼图\", fontdict=None, loc='center')\n",
    "ax2.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax2.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 直方图\n",
    "反映变量“每天晚上睡多长时间-小时”(b18a)的分布情况。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\97657\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2785: DtypeWarning: Columns (20,22,23,25,28,29,39,49,74,124,125,126,127,128,129,130,131,138,140,141,147,160,161,162,165,170,174,175,176,177,179,180,181,182,183,184,188,191,194,195,196,199,221,222,223,224,251,252,254,289,290,294,295,296) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('CEPS.csv',encoding='gb2312')\n",
    "df = pd.DataFrame(df)\n",
    "x1=df['b18a']\n",
    "x1=x1[:150]\n",
    "name_list = ['3', '4', '5', '6',\"7\",'8','9','10']\n",
    "num_list = [1,1,10,33,49,43,9,2]\n",
    "rects=plt.bar(range(len(num_list)), num_list, color='rgby')\n",
    "# X轴标题\n",
    "index=[0,1,2,3,4,5,6,7,8,9]\n",
    "index=[float(c)+0.4 for c in index]\n",
    "plt.ylim(ymax=50, ymin=0)\n",
    "plt.xticks(index, name_list)\n",
    "plt.ylabel(\"arrucay(%)\") \n",
    "plt.xlabel(\"sleeping\")\n",
    "for rect in rects:\n",
    "    height = rect.get_height()\n",
    "    plt.text(rect.get_x() + rect.get_width() / 2, height, str(height)+'%', ha='center', va='bottom')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 柱图\n",
    "反映变量\"你妈妈是做什么工作的\"(b08a)的职业分布情况，数字和编码关系如下：\n",
    "\n",
    "+ 1\t国家机关事业单位领导与工作人员\n",
    "+ 2\t企业/公司中高级管理人员\n",
    "+ 3\t教师、工程师、医生、律师\n",
    "+ 4\t技术工人（包括司机）\n",
    "+ 5\t生产与制造业一般职工\n",
    "+ 6\t商业与服务业一般职工\n",
    "+ 7\t个体户\n",
    "+ 8\t农民\n",
    "+ 9 无业、失业、下岗\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "work1=sum(df.b08a=='1')\n",
    "work2=sum(df.b08a=='2')\n",
    "work3=sum(df.b08a=='3')\n",
    "work4=sum(df.b08a=='4')\n",
    "work5=sum(df.b08a=='5')\n",
    "work6=sum(df.b08a=='6')\n",
    "work7=sum(df.b08a=='7')\n",
    "work8=sum(df.b08a=='8')\n",
    "work9=sum(df.b08a=='9')\n",
    "name_list=['国家机关事业单位领导与工作人员','企业/公司中高级管理人员',\n",
    "'教师、工程师、医生、律师','技术工人（包括司机）','生产与制造业一般职工',\n",
    "'商业与服务业一般职工','个体户','农民','无业、失业、下岗']\n",
    "num_list=[work1,work2,work3,work4,work5,work6,work7,work8,work9]\n",
    "plt.barh(range(len(num_list)),num_list,tick_label=name_list)\n",
    "\n",
    "plt.xlabel('人数')\n",
    "plt.title(\"妈妈的职业分布图\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
